gene set enrichment analysis (GSEA)

What is a gene set enrichment analysis? What is a reasonable enrichment score? With the genes in common between the two groups from yesterday’s venn diagram, I want to see what functions they correspond to. There are many ways to assign functions to genes. One way is with gene ontology terms, which are standardized terms for the functions of genes. When working with model species, e.g. mouse or fly or human, standardized terms are fairly easy to acquire. I plugged them into DAVID bioinformatics database and got some output, including this chart:

DAVID_output

The highest enrichment scores with several pathways listed (signal peptides, glycoproteins, respond to wounding, etc.) have an enrichment scores around 5-6. Are these high scores or low?

What do enrichment scores mean?

According to this paper, an enrichment score represents the degree to which a set of genes is overrepresented at the top or bottom of the total list submitted. The program ranks the genes somehow. I don’t understand the ranking process. The score is calculated by walking down the list. When a gene in the set of interest is encountered, a running-sum statistic is increased. If a gene not in the set is encountered, the running-sum statistic is decreased. The running sum is based on the Kolmogorov–Smirnov test. The magnitude of the incremental increasing or decreasing depends on a correlation of the gene with the phenotype. The enrichment score is the maximum deviation from zero encountered during the random walk (see figure 1B from the paper below).

GSEA_score

Given that the list only had 87 genes, given that 39 of them have some correlation with signaling peptides, it is probably safe to say that the genes in common between these two treatment groups are involved with cell signaling (intra or intercellular).

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About Lisa Cohen

PhD student at UC Davis.
This entry was posted in Bioinformatics, Data Analyses. Bookmark the permalink.

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